The agricultural sector is evolving with the adoption of smart farming technologies, where Digital Twins (DTs) offer new possibilities for real-time monitoring, simulation, and decision-making. While previous research has explored the Internet of Things (IoT), UAVs, machine learning (ML), and remote sensing (RS) in enhancing agricultural efficiency, a systematic approach to integrating these technologies within a DTs ecosystem remains underdeveloped. This paper presents a systematic review of 167 studies published between 2018 and 2025. The objective of this study is to examine recent advancements in DTs-enabled precision agriculture and propose a comprehensive framework for designing, integrating, and optimizing DTs in smart farming. The study systematically examines the current state of DT adoption, identifies key barriers, and computational efficiency challenges, and provides a step-by-step methodology for DT implementation. The review sheds light on potential future research direction and implications for policy, with the aim to speed up the adoption of DTs-based farm management systems in their operational success and commercial viability through analysis of practical applications and future perspectives. This study presents an innovative strategy for integrating digital and physical systems into agriculture and is an important contribution to existing literature.

One-dimensional (1D) hydraulic models have been extensively used to conduct flood simulations for investigating flood depth and extent maps. However, the 1D models cannot simulate many other flood characteristics, such as flood velocity, duration, arrival time and recession time when the flow is not restricted within the channel. These flood characteristics cannot be disregarded as they play an important role in developing flood mitigation and evacuation strategies. This study formulates a two-dimensional (2D) hydrodynamic model combined with remote sensing (RS) and geographic information system (GIS) approach to generate additional flood characteristic maps that cannot be produced with 1D models. The model was applied to a transboundary river of Deg Nullah in Pakistan to simulate an extreme flood event experience in 2014. The flood extent images from the moderate resolution imaging spectroradiometer (MODIS) and observed flood extents were used to evaluate the model performance. Moreover, an entropy distance-based approach was proposed to facilitate the integrated multivariate flood vulnerability classification. The simulated 2D flood modeling results showed a good agreement with the flood extents registered by MODIS and the observed ones. The northwest parts of Deg Nullah near Seowal, Dullam Kahalwan and Zafarwal were the most vulnerable areas due to high flood depths and prolonged flooding duration. Whereas high flood velocities, short flood arrival time, prolonged flood duration and recession times were observed in the upper reach of Deg Nullah thereby making it the most susceptible, critical and vulnerable region to flooding events.

The rising intensity and frequency of extreme temperature events are caused due to climate change and are likely to affect the entire world. In this context, the Himalayas are reported to be very sensitive to changes in temperature extremes. In this study, we investigate the variability of temperature extremes over the Northwest Himalayas in the early 21st century (2000–2018). Here, we used 14 temperature indices recommended by ETCCDI (Expert Team on Climate Change Detection and Indices). The present study reveals the trends of extreme temperature indices on the spatial scale for the western part of the Northwest Himalayas. The 14 temperature indices were used to assess the behavior of extreme temperature trends with their significance. This study reports that the northwestern region of the study area has a cooling effect due to an increase in the trends of cold spells, cold days/nights, and frost days, while the southwestern region significantly shows the warming effects due to the increasing trends in warm spells, warm days/nights, and summer days. On the other hand, the eastern region of the study area shows mixed behavior, i.e., some places show warm effects while some reveal cold effects in the early 21st century. Overall, this study implies the northwestern parts have cooling trends while the southwestern and southeastern parts have warming trends during the early 21st century.

Climate change poses a serious risk to wheat farmers in many regions of the world. The present study was conducted in the Sialkot District, Punjab, Pakistan, to investigate climate change trends during the past thirty years and to determine farmers’ knowledge and perceptions about climate change. The study also addresses the impacts of climate change on wheat production, current adaptation strategies, and limitations in adaptations to climate-smart agriculture (CSA) through a questionnaire-based survey. The historical weather data from the past thirty years indicated an increase in the mean annual minimum and maximum temperature and a decrease in annual total precipitation. Wheat productivity during the past thirty years showed an increasing trend but it was inconsistent. The respondents’ perception of climate change indicated that the literate farmers and those with broad farming experience were more knowledgeable about the climatic effects on wheat production. However, the survey results showed that the age of the farmers did not affect their perceptions. The current management practices are primarily based on prior experiences (70%) and traditional practices (30%). The standard management practices to increase farm productivity include an increase in fertilizer use (70%), a decrease in manure use (24%), and intercropping or switching to other crop cultivations (60%). The farmers stated that their reasons for limited adaptation to climate smart farm practices (CSFP) were due to their lack of knowledge and skills (86%), lack of modern technologies (74%), economic constraints (78%), politics (86%), and social influences (74%). Based on the survey results, the study suggests that addressing these gaps can increase farm-level wheat productivity to increase resilience. This can be achieved by introducing stateof- the-art farming practices through farmer training and by providing institutional services with a focus on climate-specific farm consultation services, leading to climate-smart agricultural practices for improved food security. Highlights - Literate farmers are more aware of climate change as compared to illiterate farmers. - The farmers emphasized the increase in both the summer and winter temperature. - Rainfall is identified as a major climate threat in the study area. - The farmers identified that the highest impact of climate change occurred during the harvest phase of wheat. - The farmers stated that the limited adoption of climate smart agricultural practices is due to lack of knowledge and technological, economic, and other gaps.